During cross-validation, the training set was split into five folds at every iteration, where one fold was kept as a validation set to test the performance with the trained parameters.
For the classification of Musky OC, we applied an oversampling method to the training data, excluding the validation set, to reduce the imbalance in the data distribution. The weighted F1 score was used as an evaluation metric because of the imbalanced characteristic of the datasets.
We configured the hyperparameter search space consistent with the models of [1]} and a specific range for the regularization term \(\lambda \)  for our model. We utilized the Optuna framework [2]} by repeating the cross-validation 100 times.
After finding the best-performing number of hidden nodes of the KCNet, we set it as the number of hidden nodes for both the DOA-applied model and the Ensemble-DOA-applied model, and we used the same value of the regularization term \(\lambda \)  as was used for KCNet. Details of the methods, hyperparameter search spaces, and implementations are given in Appendix REF .
